Gender: Male 
Age: 25-34
Nationality: United States
Ethnicity: Hispanic 
Marital status: Single 
First language: English 
Religion: Nothing 
Education: Undergraduate degree
Mother's education: High school or below
Father's education: High school or below
Self-assessed socioeconomic status (1-10): 6
Home: Rent 
Employment: Employed full time
Occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Mother's occupation: Service and sales workers (whose who provide personal and protective services related to travel, housekeeping, catering, personal care, protection against fire and unlawful acts)
Father's occupation: Service and sales workers (whose who provide personal and protective services related to travel, housekeeping, catering, personal care, protection against fire and unlawful acts)
Hobbies: Listen to classical music;Exercise;Watch sports 
Devices used daily: Laptop;Smartwatch;Smartphone 
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Text-to-Speech (i.e, computers being able to read out loud written language);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Dialog technology (e.g. chatbots that communicate with you such as chatGPT) 
NLP tools used: Spell checker (i.e., correcting misspellings);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT) 
NLP tools they would find useful: Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
Frequency of LLM use: Nearly every day
LLMs used: ChatGPT;Grok;Google Gemini;Microsoft Bing AI 
LLM use cases: Coding;Solving math or logical problems;Analyze data;Generate art;Completing assignments;Learning (e.g., explaining complex concepts in simple terms) 
Contexts of LLM use: Work;School/University;Entertainment;Learning;Personal;Technical 
Prompt 1: spatial resolution in mammography can be measured in terms of
Prompt 2: summarize this article for me https://www.algolia.com/blog/ai/large-language-models-llms-vs-generative-ai-whats-the-difference
Prompt 3: what to put on your resume if you were in the army for 10 years
Prompt 4: can a Geiger counter be uses to measure scatter in an xray room?
Prompt 5: what happens if you triple the kvp
Prompt 6: x rays why is kvp exponential but mAs is linear
Prompt 7: how to write python code where you input 8 tennis players playing on 4 courts and having the program randomize opponents and assign the players to a court
Prompt 8: how to get a cat to like you
Prompt 9: help me decide, should I get subway or Chinese food for dinner
Prompt 10: what are the odds of there being alien life in the universe 
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